Journal of Biogeography (J. Biogeogr.) (2013) 40, 117–130 ORIGINAL Snails on oceanic islands: testing the ARTICLE general dynamic model of oceanic island biogeography using linear mixed effect models Robert A. D. Cameron1,2, Kostas A. Triantis3,4,5*, Christine E. Parent6, Franc¸ois Guilhaumon3,7, Marı´a R. Alonso8, Miguel Iba´n˜ez 8, Anto´nio M. de Frias Martins9, Richard J. Ladle4,10 and Robert J. Whittaker4,11 1Department of Animal and Plant Sciences, ABSTRACT University of Sheffield, Sheffield, S10 4TN, Aim We collate and analyse data for land snail diversity and endemism, as a UK, 2Department of Zoology, The Natural means of testing the explanatory power of the general dynamic model of oce- History Museum, London, SW7 5BD, UK, 3Azorean Biodiversity Group, University of anic island biogeography (GDM): a theoretical model linking trends in species Azores, 9700-851, Angra do Heroı´smo, immigration, speciation and extinction to a generalized island ontogeny. 4 Terceira, Azores, Portugal, Conservation Location Eight oceanic archipelagos: Azores, Canaries, Hawaii, Gala´pagos, Biogeography and Macroecology Programme, Madeira, Samoa, Society, Tristan da Cunha. School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, UK, Methods Using data obtained from literature sources we examined the power 2 5Department of Ecology and Taxonomy, of the GDM through its derivative ATT model (i.e. diversity met- 2 Faculty of Biology, National and Kapodistrian ric = b1 + b2Area + b3Time + b4Time ), in comparison with all the possible University, Athens, GR-15784, Greece, simpler models, e.g. including only area or time. The diversity metrics consid- 6Section of Integrative Biology, University of ered were the number of (1) native species, (2) archipelagic endemic species, Texas, Austin, TX 78712, USA, 7‘Rui and (3) single-island endemic species. Models were evaluated using both log- Nabeiro’ Biodiversity Chair, CIBIO – transformed and untransformed diversity data by means of linear mixed effect Universidade de E´vora, Casa Cordovil, 7000- models. For Hawaii and the Canaries, responses of different major taxonomic 8 890, E´vora, Portugal, Departamento de groups were also analysed separately. Biologı´a Animal, Universidad de La Laguna, 2 E-38206 La Laguna, Tenerife, Canaries, Results The ATT model was always included within the group of best models Spain, 9CIBIO-Ac¸ores, Department of Biology, and, in many cases, was the single-best model and was particularly successful University of the Azores, 9501-801 Ponta in fitting the log-transformed diversity metrics. In four archipelagos, a hump- Delgada, Azores, Portugal, 10Institute of shaped relationship with time (island age) is apparent, while the other four Biological Sciences and Health, Federal archipelagos show a general increase of species richness with island age. In University of Alagoas, AL, Brazil, 11Centre for Hawaii and the Canaries outcomes vary between different taxonomic groups. Macroecology, Evolution and Climate, Main conclusions The GDM is an intentionally simplified representation of Department of Biology, University of Copenhagen, DK-2100, Copenhagen, environmental and diversity dynamics on oceanic islands, which predicts a Denmark simple positive relationship between diversity and island area combined with a humped response to time. We find broad support for the applicability of this model, especially when a full range of island developmental stages is present. However, our results also show that the varied mechanisms of island origins and the differing responses of major taxa should be taken into consideration when interpreting diversity metrics in terms of the GDM. This heterogeneity is reflected in the fact that no single model outperforms all the other models for all datasets analysed. *Correspondence: Kostas A. Triantis, Azorean Keywords Biodiversity Group, University of Azores, 9700- General dynamic model, island biogeography theory, island evolution, land 851 Angra do Heroı´smo, Terceira, Azores, Portugal. snails, mixed effect models, model selection, oceanic islands, speciation, spe- E-mail: [email protected] cies diversity dynamics. ª 2012 Blackwell Publishing Ltd http://wileyonlinelibrary.com/journal/jbi 117 doi:10.1111/j.1365-2699.2012.02781.x R. A. D. Cameron et al. Martins, 2005; Alonso et al., 2006; Parent & Crespi, 2006, INTRODUCTION 2009). Many land snail species are endemics with tiny geo- Islands are prime reservoirs of land snail diversity graphical ranges, a feature that is even more apparent in snails inhabiting oceanic islands (Solem, 1984). In this paper Alan Solem (1984, p. 9) we examine the patterns of snail species diversity and ende- The biotas of oceanic islands have provided ideal material for mism of oceanic islands, within the context of the GDM, studying ecological and evolutionary processes and their analy- and we test the predictions and the generality of the mathe- sis has profoundly influenced the development of subjects matical models arising from the GDM, using first, the overall ranging from species formation to biogeography (Whittaker & faunas, and second, certain taxonomic subsets. We do so Ferna´ndez-Palacios, 2007). Whittaker et al. (2008) recently using linear mixed models, an analytical approach pioneered developed a model, the general dynamic model of oceanic in this context by Bunnefeld & Phillimore (2012). island biogeography (hereafter GDM), to account for the development of species richness on oceanic islands. Building MATERIALS AND METHODS on the dynamic equilibrium theory of MacArthur & Wilson (1967), the GDM explicitly places the outcome of the processes The archipelagos of immigration, speciation and extinction in a temporal con- text dictated by the geological development of the islands. Data from 56 islands from eight oceanic archipelagos were The GDM is based on three premises: (1) that emergent selected based on the availability of reliable faunal lists, and properties of island biotas are a function of predictable an estimated age of origin (maximum age) of each of the trends in rates of immigration, speciation and extinction; (2) islands (see Appendix S1 in Supporting Information). Oce- that evolutionary dynamics predominate in large, remote anic islands are generally considered to be those that have islands/archipelagos; and (3) that oceanic islands are rela- formed over oceanic crust and that have never been con- tively short-lived landmasses showing a characteristic nected to continental landmasses (Whittaker & Ferna´ndez- humped trend in carrying capacity over their life span. The Palacios, 2007): they are typically relatively short-lived land- model predicts that several key diversity metrics (e.g. the masses and relatively few last longer than a few million years number of native species, the number of archipelagic ende- before subsiding and/or eroding back into the sea. Despite mic species etc.) should show a general hump-shaped trend the relative simplicity of the geological history of some oce- over time, driven largely by changing carrying capacity over anic island groups, e.g. Hawaii, the dynamics of most archi- the life cycle of the island itself. As we are unable to follow pelagos are more complex than assumed within the GDM the behaviour of a single island over millions of years, Whit- (e.g. Courtillot et al., 2003; Neall & Trewick, 2008). Even for taker et al. (2008) suggest that certain predictions could be hotspot archipelagos with a more or less linear arrangement tested by examining contemporary data from across the dif- of islands, at least three distinct types have been identified, ferent aged islands of an oceanic archipelago. This, in turn, based largely on the origin of the plumes (Fig. 4 in Courtil- generates an expectation of a positive relationship between lot et al., 2003). Nevertheless, for the majority of the islands these diversity metrics and island area combined with a para- considered here, the age of origin (maximum age) is more bolic relationship with time, i.e. diversity = b1 + b2Area + or less agreed upon (below), and thus we use maximum age 2 b3Time + b4Time (where ‘Time’ is time elapsed since island for all the analyses here (Table 1). emergence and ‘Area’ is island area), provided that the archi- Island age data were derived as follows: (1) Hawaiian pelago(s) concerned display a full range of island develop- Islands: Clague (1996). (2) Gala´pagos Islands: Geist (1996 mental stages. As this is not always the case, the relationship and unpublished data). (3) Azores: although Johnson et al. with time may vary according to the extent of the geological (1998) suggest an age of just 0.8 Ma for Sa˜o Miguel, we use ages involved, from positive, to hump-shaped, or negative. 4.01 Ma and the other ages adopted by Borges et al. (2009) Thus, fits employing this ATT2 model framework can take for the rest of the archipelago in formal analyses. (4) Madei- different forms, such as simple linear area–time dependence ran group: Geldmacher et al. (2005). (5) Canary Islands: (i.e. without the quadratic term of time), or a negative Carracedo et al. (2002). In the case of Gran Canaria, an age dependence on time (i.e. with a positive relationship with of c. 3.5 Ma has been used in some previous analyses (see area and a negative relationship with time) (see Fig. 1; Whit- Whittaker et al., 2008 and discussion therein) based on the taker et al., 2008, 2010; Borges & Hortal, 2009; Cardoso hypothesis of near-complete sterilization in the catastrophic et al., 2010; Triantis et al., 2010). Relationships may also be Roque Nublo ash flow (Marrero & Francisco-Ortega, 2001). influenced by the specific ecological requirements and dis- However, Anderson et al. (2009) demonstrate that this persal powers of the taxa studied (e.g. cave-adapted Azorean hypothesis is implausible, hence we use the maximum sub- arthropods compared to other arthropods; see Borges & aerial age of Gran Canaria, i.e. c. 14.5 Ma (Carracedo et al., Hortal, 2009). 2002; for discussion see Ferna´ndez-Palacios et al., 2011). (6) Land snails are among the better known and studied Samoan Islands: Workman et al.